عنوان مقاله:

نویسندگان:

Ahmad Ansarifar

In the field of applied linguistics (AP), similar to other scientific disciplines, diverse issues have gained popularity on different time spans. Therefore, knowing the key popular themes of the field for the researchers and the students sounds to be beneficial. The present study aims to identify the key current themes in AP research. To this end, the novel approach of topic modelling, a collection of machine learning techniques that automatically identify thematic information in large corpora was used. To do so, a one-million-word corpus of RA abstracts from top-tier journals of AP in a ten-year time span (2010-2019) was compiled. The abstract section was selected as sampling unit since the abstract is the essence of an RA and includes all important themes it contains. Moreover, its length makes it a good candidate for topic modelling. This study made use of Mallet program (McCallum, 2002) to run topic modelling. The analysis included two phases: first, the quantitative phases in which the software was instructed to extract 20 topics each including 20 keywords which is the common practice in topic modelling studies. Second, the qualitative phase in which each topic was interpreted based on the researcher s inspection of each list. Finally, Pedagogical implications for future research in AP were suggested.

Topic modeling, Research trends, Applied Linguistics

برای لینک‌دهی به این مقاله از آدرس زیر استفاده نمایید:

برای ارجاع در منابع از عبارت زیر استفاده کنید:
Ansarifar، Ahmad، 1398، Mapping the Current Trends in Applied Linguistics research: A Topic Modelling Approach، اولين کنفرانس بين المللي مطالعات زبان انگليسي، https://conf.ui.ac.ir/fa/archive_index.php?c=icels&aid=1505882